Advances in electronic media and commerce have had a significant impact on consumers by providing them with rapid access to content and the ability to find and purchase a multitude of items without having to travel to a store. Electronic media and commerce are competing heavily with traditional forms of content delivery (e.g. print and broadcast content) and “bricks and mortar” stores. A consumer can receive a significant portion of their information completely from electronic means, including electronic newspapers, e-mail, web sites, digitally stored video programming, and other electronic methods of delivery. As applied to shopping, consumers can search for, locate and purchase a tremendous number of items ranging from drugstore type items to large items, such as furniture and appliances, over the Internet.
As electronic access to information and goods has increased, recommendation engines have been developed that provide suggestions for both information and goods to consumers. These recommendation engines have been created both because electronic media and commerce provide overwhelming opportunities to consumers and because electronic media is not viewed the same as printed media. Electronic access provides more choices for information or goods than printed media (e.g. newspapers and catalogs) but does generally not provide for as rapid access to content since each page in the electronic medium must be loaded separately. To date, printed media offers faster access to content via manual page turning than electronic media offers via page loading.
As electronic media evolves and improvements are made to displays and servers, and as bandwidth to the consumer increases, the gap between print media and electronic media will begin to close. Electronic media will begin to provide a more print-like experience as consumers are able to rapidly access materials that appear to be printed on displays that may have form factors more similar to books and newspapers. Technologies such as flexible displays, tablet computers, and “smart ink” systems that appear as printed materials but which can be written to as displays have the potential to blur the line between printed and electronic media.
Printed media and electronic media are currently at opposite extremes with regards to the degree of personalization. Printed media is typically uniform: newspapers and catalogs are generally identical for all consumers. Electronic media is typically highly personalized, with the media (portal, web pages) being highly customized based on the user's preferences.
With respect to generalized or non-personalized media such as print newspapers, an individual consumer typically expects to see the same content as other consumers so that they can feel that they are receiving the same information as other consumers. As an example, a businessperson expects to see the same news items in the newspaper as other businesspeople, and would potentially be displeased by finding out that their newspaper did not contain articles that another businessperson saw. The same consumer may find personalization of a leisure magazine or catalog acceptable, however, and may prefer to have only personalized information in those publications (print or electronic). The degree of personalization may vary depending on the individual, the content, and the type of publication.
As the gap between printed media and electronic media closes, and as electronic media begins to appear closer to printed media, the degree of personalization of the content will need to be carefully considered for each application and consumer. Recommendation engines have been partially effective in sorting through the myriad of electronic choices in many applications, but are inadequate in terms of presenting the consumer with choices that are personalized enough to avoid wasting their time, yet are not overly filtered, robbing them of the shared experience printed media currently provides. What is required is a recommendation engine that allows for a sufficient degree of personalization for the specific individual and application.
Recommendation engines also suffer from the fact that they can frequently be led astray and may incorrectly perceive a like or dislike of an individual, resulting in numerous incorrect and potentially annoying recommendations. Once the recommendation engine incorrectly perceives something about the consumer, it can be difficult to escape or correct the particular characterization the system has made. What is required is a recommendation engine that can relearn the interests of the consumer without being cleared.